Survey
Scott Brenstuhl
February 23, 2016
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## layout##
## 88888 94000 94014 94101 94106 94113 94119 94120 94125 94126 94138 94142
## 8 1 2 154 43 200 120 106 110 138 1 1
## 94147 94158 94159 94160 94165 94199 99999 <NA>
## 4 17 1 1 1 13 36 342##
## 88888 94000 94014 94101 94102 94103 94104 94105 94106 94107 94108 94109
## 8 1 2 154 1349 861 39 199 43 998 854 2273
## 94110 94111 94112 94113 94114 94115 94116 94117 94118 94119 94120 94121
## 3240 332 2387 200 1741 1462 1777 1954 1595 120 106 1596
## 94122 94123 94124 94125 94126 94127 94129 94130 94131 94132 94133 94134
## 2109 1121 1170 110 138 770 62 36 1261 967 964 1211
## 94138 94142 94147 94158 94159 94160 94165 94199 99999 <NA>
## 1 1 4 17 1 1 1 13 36 2521## Source: local data frame [390 x 3]
## Groups: zipcode
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## zipcode year n()
## 1 88888 2009 8
## 2 94000 1998 1
## 3 94014 2000 2
## 4 94101 2000 1
## 5 94101 2004 2
## 6 94101 2013 151
## 7 94102 1996 71
## 8 94102 1997 25
## 9 94102 1998 75
## 10 94102 1999 91
## .. ... ... ...## [1] "integer"Ideas: Things by zipcode over time Parents vs not % of respondents with kids over the years (maybe also by zip) Peoples own neighborhoods vs city wide (by zip too) Correlation between cleanliness, lights, etc. and safety Match up Zipcode info with census data see how incomes etc. play in Tell the story of the mission & SOMA since people talk about them changing so much Mission zipcode: 94110 SOMA: 94103 Do changes over the years match up with 311 call learnings? All kinds of demographics info. Does it line up with Census data? People per household, up or down? Own or rent home What do people say in open ended comments? Transit exploration
Questions: How do I do the weighting for finweigh Neighborhoods mapped to zips? In 2015 they switched to using districts intead of zipcodes… whyyyyyy
Page 133 -135 have a lot of methodology info
regression table Arima, forcast package
6 and 7 should probably be made NA
## swclcity swclnbrd
## swclcity 1.0000000 0.4475023
## swclnbrd 0.4475023 1.0000000## Warning: Removed 12 rows containing missing values (geom_point).
## Warning: Removed 10 rows containing missing values (geom_point).
This needs to be a lot more digestable to be useful 2015, zip = NA remove after finding out why 
Lolllllllll Need a lot of zip cleaning and a better plan than facets
The groupings in this changes so need to account for that.
## Source: local data frame [35,806 x 276]
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## id year mode language finweigh swclnbrd swclcity stclnbrd
## 1 201510164 2015 1 1 1.010167 2 NA 3
## 2 201511631 2015 1 1 0.853352 3 NA 4
## 3 201511630 2015 1 1 0.853352 5 NA 5
## 4 201511629 2015 1 1 1.010167 5 NA 4
## 5 201511628 2015 1 1 0.695734 4 NA 4
## 6 201511627 2015 1 1 0.853352 NA NA NA
## 7 201511626 2015 1 1 1.598978 NA NA NA
## 8 201511625 2015 1 1 1.720994 NA NA NA
## 9 201511624 2015 1 1 0.740437 3 NA 3
## 10 201511623 2015 1 1 1.010167 NA NA NA
## .. ... ... ... ... ... ... ... ...
## Variables not shown: stclcity (chr), stpvnbrd (int), stpvcity (chr),
## treesnbr (chr), treescit (chr), cityligh (int), signsig (int), infrastr
## (chr), willtree (chr), parkgr (int), parkfa (int), reccon (int),
## recproad (chr), recproch (chr), parkvis (int), fqctpk (chr), recpart
## (int), recint (chr), recstaff (int), athfield (int), golfcour (chr),
## walkbike (chr), aqucent (chr), recpract (int), recparsy (chr), parkrecs
## (int), libbo (int), libsta (int), libacadu (chr), libac18 (chr), libmai
## (int), libbra (int), fqmain (chr), fqbran (chr), fqany (chr), libonlin
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## oftnbike (chr), bikelane (chr), bikesept (chr), nicescap (chr), bikesafe
## (chr), noencbik (chr), safdnb (int), safnnb (int), safcro (chr), crimvi
## (chr), reptvi (chr), crimnv (chr), reptnv (chr), k0to5 (int), k6to13
## (int), k14to17 (int), kids (int), knosch (int), kpubsch (int), kprisch
## (int), kqschl (int), ccare (int), asprog (int), enrich (int), yempl
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## kidex613 (chr), kidsumpr (int), empcarde (chr), kid1418 (chr), tutoring
## (int), gnocart (chr), grnfood (chr), grnpaper (chr), grnyard (chr),
## grncart (chr), discart (chr), distime (chr), dismess (chr), dispest
## (chr), disown (chr), disother (chr), rhpmhdst (chr), rhpmhbl (chr),
## rhpmhdeaf (chr), rhpmhcil (chr), rhpmhmen (chr), rhpmhcog (chr),
## enrolhea (chr), healinsu (chr), resinspr (chr), empinspr (chr), spoinspr
## (chr), medinspr (chr), othinspr (chr), pchome (chr), nethome (chr),
## netdsl (chr), netdial (chr), netwire (chr), netdk (chr), ipurch (chr),
## iconv (chr), iprod (chr), ipric (chr), itax (chr), complib (chr),
## comppark (chr), compwd (chr), compcafe (chr), compwork (chr), compnone
## (chr), socnetwk (chr), shdwvide (chr), dlivedsf (int), dage (int),
## dethnic (int), deduc (int), dempsum (chr), dincome (int), dhouse (int),
## ownrenhm (int), gender (int), dsexornt (int), zipcode (chr), district
## (int), region (chr), movesf (int), chgemp5y (chr), coverexp (chr),
## covdebt (chr), covsave (chr), covwork (chr), covnone (chr), senrmeal
## (chr), senrpers (chr), senrbene (chr), senrsocl (chr), general (int),
## cityweb (chr), citycable (chr), citysfchr (chr), citysfex (chr),
## citycomn (chr), citytv (chr), cityweek (chr), cityrad (chr), citypub
## (chr), webgovsv (chr), cont311 (int), use311sv (int), info311 (int),
## svc311 (int), infowebm (int), svcwebmo (int), broch (chr), radtv (chr),
## frico (chr), comgr (chr), other (chr), thres (chr), contempl (chr),
## deptcont (chr), easedept (chr), courprof (chr), qresolve (chr), custosvc
## (chr), emplyctr (chr), assitcbo (chr), assitclg (chr), citinfo (chr),
## prpdfood (int), prpdfam (int), prpdcpr (int), prpdnone (int), tapqual
## (chr), taptast (chr), budfirep (chr), budhlthp (chr), budhump (chr),
## budmentp (chr), budrecp (chr), budpolp (chr), budmunp (chr), budstrtp
## (chr), budfired (chr), budhlthd (chr), budhumd (chr), budmentd (chr),
## budrecd (chr), budpold (chr), budmund (chr), budstrtd (chr), opencomm
## (chr), HH (chr), wtrswr (int), swcndnhd (int), swcndcity (chr),
## netlibsvc (int), trspwlk (int), trsppub (int), trspbike (int), trsptaxi
## (int), trspdaln (int), trspcpl (int), trspptns (int), prpdcinfo (int),
## prpdctool (int), iamobile (chr), asprivpub (chr), asfrpay (chr),
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## (chr), dethhisp (chr), darab (chr), dothlnghm (chr), dtrbctylb (chr),
## dempstat (chr), langint (chr), group (int), walkhike (int), trspuber
## (int), mun12mth (int), munmgmtcro (int), disablephys (int), disablement
## (int), v128 (chr), com1 (int), com2 (int), com3 (int), com4 (int), com5
## (int), com6 (chr), mixed_1 (int), mixed_2 (int), mixed_3 (int), mixed_4
## (int), prpdother (int), primlang_1 (int), primlang_2 (int), primlang_3
## (int), primlang_4 (int), emplytype (int)
## Warning: Removed 60 rows containing missing values (geom_point).## Warning: Removed 52 rows containing missing values (geom_path).
##
## 88888 94000 94014 94101 94106 94113 94119 94120 94125 94126 94138 94142
## 8 1 2 154 43 200 120 106 110 138 1 1
## 94147 94158 94159 94160 94165 94199 99999
## 4 17 1 1 1 13 36